Skip to content

How it works

A Clearer Workflow for Data Analysis

Five steps take you from a raw file to documented, shareable results. Each step uses transparent methods you can verify.

STEP 01

Import

Bring a structured dataset into your private workspace.

  • Upload a CSV file (.csv, or delimited .tsv/.txt) or a JSON file containing an array of objects.
  • Comma, semicolon, tab and pipe delimiters are detected automatically. The first row of a CSV is used as the header.
  • Limits: 5 MB per file, 50,000 rows, 100 columns, and 50 datasets per account.
  • The file travels over HTTPS, is validated and parsed on our server, and is stored in your account. Only you can access it.
  • Excel workbooks are not read directly — save them as CSV first.
Dataset overview
Sample data

Records

1,122

Columns

10

Valid cells

99.6%

  • order_idText
  • order_dateDate
  • regionCategorical
  • categoryCategorical
  • channelCategorical
  • unitsNumeric
  • unit_priceNumeric
  • discount_pctNumeric
  • revenueNumeric
  • customer_ratingNumeric
Computed from “Sample Retail Orders 2025 (synthetic)”, a synthetic demonstration dataset.
STEP 02

Prepare

Understand what was imported before you analyze it.

  • Each column is classified as numeric, date, boolean, categorical or text using documented rules — for example, a column is numeric when at least 95% of its values are numbers.
  • Dates are recognized in the unambiguous YYYY-MM-DD and YYYY/MM/DD formats, so 03/04/2025 is never silently misread.
  • The data-quality panel reports missing values, exact duplicate rows, values that do not match their type, and the columns that need attention.
  • Create a copy without duplicate rows, rename datasets, and delete what you no longer need.
Data quality
Sample data

Missing values

47

Duplicate rows

4

Invalid values

2

Columns needing attention

  • region — 1 missing value(s)
  • category — 1 missing value(s)
  • unit_price — 2 value(s) do not match the number type (e.g. “n/a”)
  • customer_rating — 45 missing value(s)
Computed from “Sample Retail Orders 2025 (synthetic)”, a synthetic demonstration dataset.
STEP 03

Analyze

Run calculations with clearly stated methods.

  • Descriptive statistics: count, sum, mean, median, mode, min, max, range, variance and standard deviation (sample, n − 1), quartiles, percentiles and skewness.
  • Frequency analysis: counts, percentages and cumulative percentages for any column, plus histograms for numeric columns.
  • Trends: aggregate a metric by day, week, month, quarter or year and measure period-over-period change.
  • Patterns: correlations, unusual values, group comparisons and cross-tabulations.
Descriptive statistics · revenue
Sample data
Count
1,122
Mean
473.56
Median
184.88
Std. deviation
775.96
Minimum
2.7
Q1 (25th pct.)
57.59
Q3 (75th pct.)
502.47
Maximum
5,719.99
Skewness
3.17

0 invalid and 0 missing value(s) excluded · sample standard deviation (n − 1)

Computed from “Sample Retail Orders 2025 (synthetic)”, a synthetic demonstration dataset.
STEP 04

Explore

Narrow down, compare and interpret.

  • Filter records by text, number or date conditions — equals, contains, greater or less than, between, empty — combined with AND.
  • Interactive charts update as you change columns, intervals, bins or filters; tooltips show exact values.
  • Compare a metric across categories or between two date ranges.
  • Rule-based automated observations highlight skewed columns, strong correlations and quality issues, each with the rule that produced it.
Trend · monthly sum of revenue
Sample data
2025-012025-12

Highest · 2025-11

69,530

Lowest · 2025-08

18,435

Computed from “Sample Retail Orders 2025 (synthetic)”, a synthetic demonstration dataset.
STEP 05

Export

Keep and share what you found.

  • Save analyses to History and reopen them later with the same settings.
  • Create reports with methodology, results, charts, observations and limitations.
  • Download tables as CSV, and print reports or save them as PDF through your browser.
  • Export all of your account data as JSON from Settings at any time.
Report preview
Sample data

Analysis report

Units by region — summary

Dataset: Sample Retail Orders 2025 (synthetic)

Methodology: Descriptive statistics and frequency counts on non-missing values.

Statistics: units — mean 4.85, median 3, max 24

Distribution: North 22.4% · East 21.8% · West 21.4%

Limitations: Results describe this dataset only and do not predict future values.

Print / Save as PDFExport CSV
Computed from “Sample Retail Orders 2025 (synthetic)”, a synthetic demonstration dataset.

Practical questions

Before you start

Short answers to the questions we hear most about the workflow.

Do I need programming experience?
No programming experience is required for the supported analytical workflows. You choose columns and options from menus; formulas, grouping and charts are handled for you.
What kind of data can I analyze?
EuroData Score works with structured, tabular datasets — one record per row — containing numerical, categorical, boolean and date-based information, supplied as CSV or JSON up to 5 MB. It does not read Excel workbooks, images, PDFs or free-form documents.
Can I export my results?
Yes. Tables export as CSV files, reports can be printed or saved as PDF through your browser's print dialog, and your account data can be exported as JSON from Settings.
Can the results predict what will happen next?
No. The tools describe patterns in the data you provide. Historical trends and frequencies do not guarantee future outcomes.

Want to see each step with real sample data first? Open the interactive demo — no account required. Every illustration on this page is computed from the same synthetic sample dataset.

Ready to analyze your own data?

EuroData Score — Full Access is a one-time payment of €42. No subscription, no recurring charges, and a 60-day money-back guarantee.